Reviewing North American Gas and Oil Field Distribution
Justin Napolitano
2022-05-05 18:40:32.169 +0000 UTC
Table of Contents
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North American Gas and Oil Fields
North American gas and oil fields contain spent wells that can be used to store carbon and green hydrogen. They may also potentially yield more resources with the injection of super critical co2 into the wells.
An analysis of the capcity of wells requires an overview of the fields in general. In this post, I identify the gas fields to later create a model that will predict the price of storing carbon and possibly hyrdogen in spent wells.
Data Import
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon
/Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
warnings.warn(
Oil and Natural Gas Field Data
## Importing our DataFrames
gisfilepath = "/Users/jnapolitano/Projects/data/energy/Oil_and_Natural_Gas_Fields.geojson"
fields_df = gpd.read_file(gisfilepath)
na = fields_df.PR_OIL.min()
fields_df.replace(na, 0 , inplace=True)
fields_df = fields_df.to_crs(epsg=3857)
fields_df.describe()
| OBJECTID | PR_OIL | PR_GAS | SHAPE_Length | SHAPE_Area | |
|---|---|---|---|---|---|
| count | 224.000000 | 224.000000 | 224.000000 | 224.000000 | 224.000000 |
| mean | 112.500000 | 1530.496585 | 87.685987 | 16.473665 | 10.386605 |
| std | 64.807407 | 17219.764834 | 644.145040 | 43.284473 | 45.170003 |
| min | 1.000000 | 0.000000 | 0.000000 | 0.100238 | 0.000594 |
| 25% | 56.750000 | 0.000000 | 0.000000 | 2.511389 | 0.221885 |
| 50% | 112.500000 | 0.000000 | 0.000000 | 6.563044 | 1.225873 |
| 75% | 168.250000 | 0.000000 | 0.000000 | 14.317886 | 5.378536 |
| max | 224.000000 | 238050.000000 | 8446.000000 | 485.692251 | 448.052251 |
.. index::
single: Oil/Gas Fields Map by Commodity
Oil Gas Field Map by Commodity
fields_map =fields_df.explore(
column="COMMODITY", # make choropleth based on "PORT_NAME" column
popup=False, # show all values in popup (on click)
tiles="Stamen Terrain", # use "CartoDB positron" tiles
cmap='Reds', # use "Set1" matplotlib colormap
#style_kwds=dict(color="black"),
marker_kwds= dict(radius=6),
tooltip=['NAICS_DESC','REGION', 'COMMODITY' ],
legend =True, # use black outline)
categorical=True,
)
fields_map
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